\n| Team<\/td>\n | Participant<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Now fetch upcoming fixtures for a given sport and tournament:<\/p>\n import time\n\ndef fetch_fixtures(sport_id, tournament_id):\n params = {\n \"apiKey\": API_KEY,\n \"sportId\": sport_id,\n \"tournamentId\": tournament_id,\n \"limit\": 300\n }\n r = requests.get(f\"{BASE}\/fixtures\", params=params, timeout=15)\n r.raise_for_status()\n data = r.json()\n now = time.strftime(\"%Y-%m-%dT%H:%M:%S\")\n # Filter to upcoming fixtures with odds available\n return [f for f in data\n if f.get(\"startTime\", \"\") >= now\n and f.get(\"hasOdds\")][:20]\n<\/code><\/pre>\nWe filter for fixtures where hasOdds<\/code> is true \u2014 no point fetching odds data for fixtures that don’t have any yet. We also cap at 20 to keep API usage manageable on the free tier.<\/p>\nStep 4: Pull Odds from All Bookmakers<\/h3>\nThis is where OddsPapi’s power becomes obvious. One API call returns odds from every available bookmaker for a given fixture. The JSON is deeply nested \u2014 here’s the exact path to a price:<\/p>\n bookmakerOdds[slug] \u2192 markets[marketId] \u2192 outcomes[outcomeId] \u2192 players[\"0\"] \u2192 price<\/code><\/p>\ndef fetch_odds(fixture_id):\n r = requests.get(f\"{BASE}\/odds\",\n params={\"apiKey\": API_KEY, \"fixtureId\": fixture_id},\n timeout=15)\n r.raise_for_status()\n return r.json().get(\"bookmakerOdds\", {})\n\ndef extract_prices(bookmaker_odds, market_id, outcomes):\n # Extract prices from all bookmakers for a given market.\n prices = {}\n for slug, book_data in bookmaker_odds.items():\n market = book_data.get(\"markets\", {}).get(market_id, {})\n outs = market.get(\"outcomes\", {})\n book_prices = {}\n for oid, label in outcomes.items():\n price = (outs.get(oid, {})\n .get(\"players\", {})\n .get(\"0\", {})\n .get(\"price\"))\n if price and price > 1:\n book_prices[label] = price\n if book_prices:\n prices[slug] = book_prices\n return prices\n<\/code><\/pre>\nA few things to note:<\/p>\n \nmarket_id<\/code> is a string key in the JSON (not an integer), so pass \"101\"<\/code> not 101<\/code><\/li>\n- The
players[\"0\"]<\/code> key is literal \u2014 it’s always the string \"0\"<\/code> for standard market outcomes<\/li>\n- We filter out prices \u2264 1.0 (invalid or suspended odds appear as 1.0 in some feeds)<\/li>\n
- Soccer Full Time Result (1X2) uses market ID
101<\/code>; Basketball moneyline uses 111<\/code><\/li>\n<\/ul>\nStep 5: Line Shopping \u2014 Find Best Prices and Detect Arbitrage<\/h3>\nLine shopping is straightforward: for each outcome, find the highest decimal odds across all bookmakers. If the combined implied probability of the best prices per outcome is below 100%, you have an arbitrage opportunity.<\/p>\n def find_best_prices(prices, outcome_labels):\n # Find the highest price per outcome across all bookmakers.\n best = {}\n for label in outcome_labels:\n best_price = 0\n best_book = \"\"\n for slug, p in prices.items():\n if p.get(label, 0) > best_price:\n best_price = p[label]\n best_book = slug\n best[label] = {\"price\": best_price, \"book\": best_book}\n return best\n\ndef detect_arb(prices, outcome_labels):\n # Check if best prices create an arbitrage opportunity.\n # An arb exists when sum(1\/best_price_per_outcome) < 1.0\n # Margin = (1 - total_implied) * 100 = guaranteed profit %\n best = find_best_prices(prices, outcome_labels)\n total_implied = sum(\n 1 \/ best[lbl][\"price\"]\n for lbl in outcome_labels\n if best[lbl][\"price\"] > 0\n )\n if total_implied < 1.0:\n margin = round((1 - total_implied) * 100, 2)\n return {\"arb\": True, \"margin\": margin, \"best\": best}\n return {\"arb\": False,\n \"overround\": round((total_implied - 1) * 100, 2)}\n<\/code><\/pre>\nExample: if the best Home price is 2.10 at Pinnacle, best Draw is 3.60 at 1xBet, and best Away is 4.20 at Singbet \u2014 implied probabilities are 47.6% + 27.8% + 23.8% = 99.2%. That 0.8% gap is a guaranteed profit window, no matter the outcome.<\/p>\n OddsPapi's 300+ bookmaker coverage dramatically increases the frequency of these windows compared to tools that only show 15-40 books. More data sources = more divergence opportunities.<\/p>\n Step 6: Build the Streamlit Dashboard<\/h3>\nNow wire everything together into an interactive dashboard:<\/p>\n import streamlit as st\nimport plotly.graph_objects as go\nimport pandas as pd\n\nst.set_page_config(page_title=\"Odds Dashboard\", layout=\"wide\")\nst.title(\"Live Odds Comparison Dashboard\")\nst.caption(\"Powered by OddsPapi \u2014 300+ bookmakers\")\n\nSPORT_CFG = {\n \"Soccer \u2014 Premier League\": {\n \"sportId\": 10, \"tournamentId\": 17,\n \"marketId\": \"101\",\n \"outcomes\": {\"101\": \"Home\", \"102\": \"Draw\", \"103\": \"Away\"},\n },\n \"Basketball \u2014 NBA\": {\n \"sportId\": 11, \"tournamentId\": 132,\n \"marketId\": \"111\",\n \"outcomes\": {\"111\": \"Home\", \"112\": \"Away\"},\n },\n}\n\nsport = st.selectbox(\"Sport\", list(SPORT_CFG.keys()))\ncfg = SPORT_CFG[sport]\nauto = st.toggle(\"Auto-refresh (60s)\", value=False)\noutcome_labels = list(cfg[\"outcomes\"].values())\n\nfixtures = fetch_fixtures(cfg[\"sportId\"], cfg[\"tournamentId\"])\n\nfor fx in fixtures[:5]:\n name = f\"{fx['participant1Name']} vs {fx['participant2Name']}\"\n bo = fetch_odds(fx[\"fixtureId\"])\n prices = extract_prices(bo, cfg[\"marketId\"], cfg[\"outcomes\"])\n best = find_best_prices(prices, outcome_labels)\n arb = detect_arb(prices, outcome_labels)\n\n st.markdown(f\"### {name}\")\n st.caption(f\"{len(prices)} bookmakers available\")\n\n # Best price metrics\n cols = st.columns(len(outcome_labels))\n for i, lbl in enumerate(outcome_labels):\n with cols[i]:\n st.metric(f\"Best {lbl}\", f\"{best[lbl]['price']:.2f}\",\n help=f\"at {best[lbl]['book']}\")\n\n # Bar chart\n featured = [\"pinnacle\", \"bet365\", \"draftkings\",\n \"fanduel\", \"bwin\", \"1xbet\"]\n fig = go.Figure()\n for slug in featured:\n if slug not in prices:\n continue\n vals = [prices[slug].get(lbl, 0) for lbl in outcome_labels]\n fig.add_trace(go.Bar(name=slug, x=outcome_labels, y=vals,\n text=[f\"{v:.2f}\" for v in vals],\n textposition=\"outside\"))\n fig.update_layout(barmode=\"group\", template=\"plotly_dark\",\n yaxis_title=\"Decimal Odds\", height=400)\n st.plotly_chart(fig, use_container_width=True)\n\n if arb[\"arb\"]:\n st.success(f\"ARB: {arb['margin']}% guaranteed profit!\")\n\nif auto:\n import time\n time.sleep(60)\n st.rerun()\n<\/code><\/pre>\nRun it with:<\/p>\n streamlit run dashboard.py<\/code><\/pre>\nStreamlit opens a browser tab automatically. The dashboard is fully interactive \u2014 change the sport, toggle auto-refresh, or expand any fixture to see all bookmakers.<\/p>\n The Complete Script<\/h2>\nThe full working script combining all functions above is available in the OddsPapi GitHub examples repository<\/a>. It includes fetch_fixtures<\/code>, fetch_odds<\/code>, extract_prices<\/code>, find_best_prices<\/code>, detect_arb<\/code>, and the Streamlit UI in a single ready-to-run file.<\/p>\nTo deploy for free on Streamlit Cloud:<\/p>\n \n- Push your
dashboard.py<\/code> and a requirements.txt<\/code> to a GitHub repo<\/li>\n- Connect the repo at streamlit.io\/cloud<\/a><\/li>\n
- Add your
API_KEY<\/code> as a Streamlit secret (st.secrets[\"API_KEY\"]<\/code>)<\/li>\n- Deploy \u2014 your dashboard is live at a public URL, refreshing automatically<\/li>\n<\/ol>\n
What to Build Next<\/h2>\nUpgrade to WebSocket for Sub-Second Updates<\/h3>\nThe REST API approach polls every 60 seconds. For in-play markets, that's too slow. OddsPapi supports real-time WebSocket streaming that pushes odds updates the moment they change. See the WebSocket Odds API guide<\/a> to upgrade your dashboard to a live feed.<\/p>\nAdd Historical Line Movement Tracking<\/h3>\nOddsPapi includes free historical odds data on the free tier \u2014 something competitors charge separately for. Store odds snapshots in a SQLite database and chart how lines move before kick-off. Pinnacle line movement is one of the most reliable signals in sports betting, and you get it free.<\/p>\n Build Telegram or Discord Alerts<\/h3>\nAdd a notification layer so you don't need the dashboard open. When detect_arb()<\/code> returns True<\/code>, fire a message to a Telegram bot or Discord webhook with the fixture, margin, and best prices.<\/p>\nAdd More Markets<\/h3>\nThis tutorial used 1X2 (market ID 101<\/code>) for soccer and moneylines for NBA. OddsPapi supports Asian Handicaps (AH 0 = 1072<\/code>, AH -0.5 = 1068<\/code>), Both Teams to Score (104<\/code>), Over\/Under 2.5 (1010<\/code>), and player props. Extend the SPORT_CFG<\/code> dict to add new markets without touching the core logic.<\/p>\nFrequently Asked Questions<\/h2>\n
|